Optimize Proactive Maintenance and Inventory control by Using the Markov Decision Process and simulation in the frame of Industrial Internet of Things (IIOT)

Implementation of maintenance programs at the right time and simultaneous management and control of inventory, taking into account changes of technology, as well as the use of new technologies, is an issue that can affect the quality of production, as Be considered a competitive advantage. The purpose of this study is to optimize the Expected Loss Rate in the two concept of "maintenance" and "inventory planning and control" based on time and cost. For this purpose, the optimal policy is proposed according to the identified events based on time and cost, using the Markov Decision Process and the values of probabilities in different states of the system. To determine the effectiveness of time and policies, the concept of Industrial IoT has been used and the problem with the OPNET simulator has been modeled and simulated, and based on the new time values, the optimal values have been calculated. For conducting the research, historical data related to the implementation of maintenance and risk assessment in the gas pipeline network have been used. Based on the change in the average occurrence rate of events, the time of simulation and change in the values of network statistical parameters, Sensitivity analysis and model validation are performed. The results of the study indicate the rate of improvement and the optimal rate of the Expected Loss Rate based on time to" implement maintenance policy ", " effect of maintenance policy " and " order spare parts and logistics spare parts", presents based on cost.